title: Metabolic syndrome clusters and the risk of incident stroke: the atherosclerosis risk in communities (ARIC) study.
abstract: OBJECTIVE
  Little is known about the metabolic syndrome (MetS) and the risk of incident stroke. This study is designed to identify particular clusters of MetS components that carry the highest risk of incident stroke.
  
  
  METHODS
  We analyzed the public use data from the population-based Atherosclerosis Risk in Communities study. At baseline, 14 993 stroke-free middle-aged individuals were followed-up over 9 years for incident stroke. MetS components were defined according to the National Heart, Lung, and Blood Institute/American Heart Association criteria. Incident stroke was identified using a standardized incident events identification and classification protocol. Proportional hazard models were used to assess the RRs and 95% CIs of ischemic stroke associated with MetS and its different clusters.
  
  
  RESULTS
  At baseline, the prevalence of MetS was 39%. The mean age was 54, with 26% blacks and 55% females. The hazard ratio of incident ischemic stroke associated with MetS among women (hazard ratio, 2.41; 95% CI, 1.69 to 3.49) and men (hazard ratio, 2.11; 95% CI, 1.56-2.85) was similar. There was a dose-response relationship between the numbers of MetS components and the risk of incidence stroke. Persons with either elevated blood pressure or elevated fasting glucose in the clusters to form a MetS had the highest risk for incident stroke (hazard ratio, 2.74-4.16 comparing to the reference group) than MetS without these 2 components (hazard ratio, </=2.00 comparing to the reference group).
  
  
  CONCLUSIONS
  The data support the need to target MetS, especially MetS, with these 2 highest risk components (elevated blood pressure or elevated fasting glucose) in the clusters.
authors.affiliation: Department of Public Health Sciences, The Pennsylvania State University College of Medicine, A210, 600 Centerview Dr, Hershey, PA 17033, USA. smr359@psu.edu
authors.forename: Sol M
authors.initials: SM
authors.lastname: Rodriguez-Colon
authors.forename: Jingping
authors.initials: J
authors.lastname: Mo
authors.forename: Yinkang
authors.initials: Y
authors.lastname: Duan
authors.forename: Jiahao
authors.initials: J
authors.lastname: Liu
authors.forename: Joanne E
authors.initials: JE
authors.lastname: Caulfield
authors.forename: Xuejuan
authors.initials: X
authors.lastname: Jin
authors.forename: Duanping
authors.initials: D
authors.lastname: Liao
mesh_terms: D002536:Cerebral Arteries / Q000378:metabolism / Q000473:pathology / Q000503:physiopathology; D016000:Cluster Analysis; D015331:Cohort Studies; D015897:Comorbidity; D048909:Diabetes Complications / Q000453:epidemiology; D016021:Epidemiologic Studies; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000453:epidemiology; D006973:Hypertension / Q000453:epidemiology; D015994:Incidence; D007333:Insulin Resistance / Q000502:physiology; D002537:Intracranial Arteriosclerosis / Q000453:epidemiology* / Q000378:metabolism / Q000503:physiopathology; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology* / Q000378:metabolism / Q000503:physiopathology; D008875:Middle Aged; D016016:Proportional Hazards Models; D012307:Risk Factors; D020521:Stroke / Q000453:epidemiology* / Q000378:metabolism / Q000503:physiopathology